The general problem of using electronic computers to analyze and interpret electronically acquired imagery involves the analysis of images that are represented by a multiplicity of individual brightness samples distributed geometrically in space. These samples are often called pixels. Each pixel represents a numerical measurement of luminosity within a small solid angle with respect to the camera. In many cases, the problem of interpreting images composed of such multiplicities of pixels is identical to the problem of associating groups or subsets of pixels within the image with physical objects within the field of view of the camera or imaging instrument which made the image.
In many cases, the great majority of pixels within a given image bear little or no useful information. The information bearing pixels within a practical image will often be those pixels which fall along the boundaries of physical objects within the image, or along the boundaries of component parts of objects within the image.
Prior art methods of boundary discovery use first and second derivatives of image brightness, in conjunction with one or more threshold levels, to transform a raw image into an “edge-enhanced” image. This “enhanced” image is then passed as input to subsequent steps in the processing algorithm. Some of these methods, such as the Canny Edge Detection Operator, use directional filters (or kernels) to emphasize edge feature in certain directions.
The present disclosure describes an improvement to such prior art methods.